ENCePP Guide on Methodological Standards in Pharmacoepidemiology

10.3.5. Data analysis

The focus of data analysis should
be on the measure of effect modification (see section 4.2.4 of this Guide on
Effect Modification). Attention should be given to whether the mode of
inheritance (e.g. dominant, recessive or additive) is defined a priori based on
prior knowledge from functional studies. However, investigators are usually
naïve regarding the underlying mode of inheritance. A solution might be to
undertake several analyses, each under a different assumption, though the
approach to analysing data raises the problem of multiple testing (see Methodological quality of pharmacogenetic studies: issues of
concern. Stat Med 2008;27(30):6547-69). The problem of multiple
testing and the increased risk of type I error is in general a problem in
pharmacogenetic studies evaluating multiple SNPs, multiple exposures and
multiple interactions. The most common approach to correct for multiple testing
is to use the Bonferroni correction. This correction may be considered too
conservative and runs the risk of producing many pharmacogenetic studies with a
null result. Other approaches to adjust for multiple testing include permutation
testing and false discovery rate (FDR) control, which are less conservative. The
FDR, described in Statistical
significance for genomewide studies (Proc Natl Acad Sci USA
2003;100(16):9440-5), estimates the expected proportion of false-positives among
associations that are declared significant, which is expressed as a q-value.